Erosion Susceptibility Mapping Research Articles

Determining of gully erosion susceptibility based on UAV and machine learning in Loess Plateau

In order to determine the susceptibility of gully erosion at a small watershed scale on the Loess Plateau of China, three hybrid models were developed. These models were based on the Multi-Attributive Border Approximation Area Comparison (MABAC), frequency ratio (FR), CatBoost (CB), LightGBM (LG), and extremely-randomized tree (ET). Based on the Unmanned Aerial Vehicles (UAV) photos, a total of 83 gullies with 12,150 gully pixels and 8 conditioning variables were extracted and used to create the gully inventory database. The correlations between the conditioning parameters and the pixels of gullies were then determined using FR, and the relative importance of these conditioning factors was quantified using machine learning. Then, for gully erosion susceptibility mapping (GESM), three hybrid gully erosion susceptibility models called MABAC-FR-CB, MABAC-FR-LG, and MABAC-FR-ET were developed. The performance of three hybrid models was assessed using the receiver operating characteristic curve (ROC) and the Kappa coefficient. The results claimed that slope steepness greatly influenced the erosion of the gully. The MABAC-FR-ET performed the most precisely, with area under curvature (AUC) of 0.998 and a Kappa of 0.952. As a result, it was determined that MABAC-FR-ET is the most exact and accurate method for predicting the susceptibility to gully erosion in the study watershed.

Integration of FuzzyAHP and machine learning algorithms for climate-driven gully erosion susceptibility mapping: predicting future trends in the eastern lateritic region, West Bengal, India

Integration of FuzzyAHP and machine learning algorithms for climate-driven gully erosion susceptibility mapping: predicting future trends in the eastern lateritic region, West Bengal, India

GIS based Soil Erosion Susceptibility Mapping in Middle Himalayas using MCDM: A Case Study of Rajouri District.

The Pirpanjal range, nestled within the Himalayan region, confronts substantial soil erosion challenges owing to its diverse topography and the inherent instability of geological formations. This study focuses on the Rajouri region, to assess soil erosion susceptibility and spatially prioritize vulnerable zones. Utilizing a Geographical Information System (GIS)-based approach, we employed the Analytic Hierarchy Process (AHP) and the Weighted Sum Method (WSM). Various datasets, including precipitation records, geological maps, soil maps, and satellite imagery, were incorporated to derive eleven critical factors. These factors encompassed topographical derivatives; land use and land cover (LULC), soil properties, drainage patterns, rainfall data, lithological characteristics, wetness index, and the vegetative health of the area. The methodology employed yielded unbiased and reliable ratings and weightages, as substantiated by a Consistency Ratio (CR) of 0.095. The results reveal that 41% of the total area in the study region is exceedingly vulnerable to soil erosion. Slope variations range from 0 to 67.14 degrees, delineating high and very high susceptibility zones spanning 531.79 km² (19.82%) and 316.20 km² (11.78%) of the area, respectively. Additionally, assessments based on the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) underscore the severity of soil erosion, covering 40% and 25% of the highly susceptible zones, respectively. High drainage density and curvature zones were identified in 13% and 31% of the study area, respectively. This comprehensive study contributes valuable insights for the planning and implementation of effective soil conservation measures in the Rajouri region.

Soil erosion susceptibility mapping of Hangu Region, Kohat Plateau of Pakistan using GIS and RS-based models

Soil erosion susceptibility mapping of Hangu Region, Kohat Plateau of Pakistan using GIS and RS-based models

Utilizing Machine Learning Algorithms for the Development of Gully Erosion Susceptibility Maps: Evidence from the Chotanagpur Plateau Region, India

Gully erosion is a serious environmental threat, compromising soil health, damaging agricultural lands, and destroying vital infrastructure. Pinpointing regions prone to gully erosion demands careful selection of an appropriate machine learning algorithm. This choice is crucial, as the complex interplay of various environmental factors contributing to gully formation requires a nuanced analytical approach. To develop the most accurate Gully Erosion Susceptibility Map (GESM) for India’s Raiboni River basin, researchers harnessed the power of two cutting-edge machine learning algorithm: Extreme Gradient Boosting (XGBoost) and Random Forest (RF). For a comprehensive analysis, this study integrated 24 potential control factors. We meticulously investigated a dataset of 200 samples, ensuring an even balance between non-gullied and gullied locations. To assess multicollinearity among the 24 variables, we employed two techniques: the Information Gain Ratio (IGR) test and Variance Inflation Factors (VIF). Elevation, land use, river proximity, and rainfall most influenced the basin’s GESM. Rigorous tests validated XGBoost and RF model performance. XGBoost surpassed RF (ROC 86% vs. 83.1%). Quantile classification yielded a GESM with five levels: very high to very low. Our findings reveal that roughly 12% of the basin area is severely affected by gully erosion. These findings underscore the critical need for targeted interventions in these highly susceptible areas. Furthermore, our analysis of gully characteristics unveiled a predominance of V-shaped gullies, likely in an active developmental stage, supported by an average Shape Index (SI) value of 0.26 and a mean Erosivness Index (EI) of 0.33. This research demonstrates the potential of machine learning to pinpoint areas susceptible to gully erosion. By providing these valuable insights, policymakers can make informed decisions regarding sustainable land management practices.

Assessing gully erosion susceptibility dynamics using information value and hazard index methods: A case study of Agulu-Nanka watershed, Southeast Nigeria

Gully erosion is one of the major causes of land degradation leading to the loss of productive lands globally. The Benue Trough in Nigeria is plagued with the problem of gully erosion, with a myriad of natural and anthropogenic factors considered as contributory factors. In this study, quantitative and qualitative methods were combined to evaluate gully susceptibility in the Agulu-Nanka watershed located in the lower part of Benue Trough between 2000 and 2018. Landsat 7 and 8 images, Digital Elevation Model (DEM), geological, soil, and rainfall erosivity maps were used to generate the gully erosion controlling factors including vegetation cover, lithology, soil, land use/cover, slope, curvature, aspect and rainfall erosivity. Additionally, high resolution image was used to develop gully inventory map. The information value method was used evaluate the influence of various classes of gully erosion controlling factors on gully distribution in the watershed. This enabled objective derivation of weights for the various classes of these factors. Information value analysis reveal that low percentage vegetation cover (<10 %), Ameki Group lithological unit, high slope gradient (17 − 43°) and Dystric Fluvisols soil type have the highest influence on gully erosion in the watershed. Since the information value method does not consider interrelationship between factors, the rank sum technique was used to derive factor weights based on the relationship between the factors. To develop the gully susceptibility map for the Agulu-Nanka watershed for the years 2000 and 2018, the erosion controlling factors were integrated using factor class weights derived through information value and factor weights derived through rank sum method. The analysis of gully erosion hazard maps show that approximately 48 % of the watershed was under low erosion risk over the period of 18 years. Areas under moderate, high and severe erosion risk decreased from 27.45 %, 9.45 % and 0.27 % of the watershed in year 2000, to 21.30 %, 8.41 %, and 0.20 % of the watershed respectively in 2018. On the other hand, the areas under negligible erosion risk increased from 14.69 % in year 2000, to 21.26 % of the watershed in 2018. The validation of the gully erosion susceptibility map for years 2000 and 2018 using the Receiver Operating Curve (ROC) showed ROC-AUC values of 0.81 % and 0.88 % respectively suggesting good model results. While the result of study indicates that restoration efforts through afforestation is yielding some result, the minimal reduction in the size of areas under severe and high gully erosion susceptibilities indicate that additional mitigation measures need to be combined with intensified afforestation efforts to effectively combat gully erosion in the watershed.

Development of an automated method for flood inundation monitoring, flood hazard, and soil erosion susceptibility assessment using machine learning and AHP–MCE techniques

BackgroundOperational large-scale flood monitoring using publicly available satellite data is possible with the advent of Sentinel-1 microwave data, which enables near-real-time (at 6-day intervals) flood mapping day and night, even in cloudy monsoon seasons. Automated flood inundation area identification in near-real-time involves advanced geospatial data processing platforms, such as Google Earth Engine and robust methodology (Otsu’s algorithm).ObjectivesThe current study employs Sentinel-1 microwave data for flood extent mapping using machine learning (ML) algorithms in Assam State, India. We generated a flood hazard and soil erosion susceptibility map by combining multi-source data on weather conditions and soil and terrain characteristics. Random Forest (RF), Classification and Regression Tool (CART), and Support Vector Machine (SVM) ML algorithms were applied to generate the flood hazard map. Furthermore, we employed the multicriteria evaluation (MCE) analytical hierarchical process (AHP) for soil erosion susceptibility mapping.SummaryThe highest prediction accuracy was observed for the RF model (overall accuracy [OA] > 82%), followed by the SVM (OA > 82%) and CART (OA > 81%). Over 26% of the study area indicated high flood hazard-prone areas, and approximately 60% showed high and severe potential for soil erosion due to flooding. The automated flood mapping platform is an essential resource for emergency responders and decision-makers, as it helps to guide relief activities by identifying suitable regions and appropriate logistic route planning and improving the accuracy and timeliness ofemergency response efforts. Periodic flood inundation maps will help in long-term planning and policymaking, flood management, soil and biodiversity conservation, land degradation, planning sustainable agriculture interventions, crop insurance, and climate resilience studies.

Implementation of random forest, adaptive boosting, and gradient boosting decision trees algorithms for gully erosion susceptibility mapping using remote sensing and GIS

Implementation of random forest, adaptive boosting, and gradient boosting decision trees algorithms for gully erosion susceptibility mapping using remote sensing and GIS

Integrated Analytical Hierarchy Process (AHP) and GIS techniques based Soil Erosion Susceptibility Mapping in Chite Watershed, Mizoram, India

Soil erosion is a significant form of land degradation that profoundly affects agricultural and ecosystem sustainability. The present study aimed at addressing this issue by developing precise and feasible erosion susceptibility map in Chite watershed, India, using integrated approach combining the Analytical Hierarchy Process (AHP) with Geographic Information System (GIS). To evaluate erosion susceptibility conditions, multiple causative factors including elevation, slope, drainage density, distance from streams, land use / land cover, rainfall intensity, normalized difference vegetation index, lithology, soil texture and lineament density were selected. These factors and their sub-classes were prioritized by AHP method based on their relative influence on soil erosion. Subsequently, the calculated AHP weights were utilized for generating a spatial dataset of soil erosion susceptibility through GIS technology. Accuracy assessment by the area under the ROC curve (AUC) reveals considerably acceptable results for the predicted map when compared to erosion inventory, producing an accuracy level of 0.812 (81%) and 0.922 (92%) for erosion and non-erosion points respectively. Thus, the present study manifests the efficiency of integrating the AHP and GIS techniques for erosion susceptibility mapping in the Chite watershed and this may serve as a valuable tool for sustainable land management and erosion control.

Erosion susceptibility mapping of a loess-covered region using Analytic Hierarchy Process – A case study: Kalat-e-Naderi, northeast Iran

In this study, the Analytic Hierarchy Process (AHP) is applied to generate erosion susceptibility maps in four basins of Kalat-e-Naderi county, namely Archangan, Kalat, Qaratigan, and Chahchaheh basins, situated in northeast Iran. The Kalat-e-Naderi region is characterized by a partial coverage of loess. Given the agricultural significance of loess and its susceptibility to erosion, this research focuses specifically on regions covered by loess. Geographic Information System (GIS) tools, including ArcMap and Quantum Geographic Information System (QGIS), were utilized to facilitate the creation of erosion susceptibility maps. Seven factors, including slope, aspect, elevation, drainage density, lithology, the Normalized Difference Vegetation Index (NDVI), and precipitation were selected for consideration. Recognizing the variability of precipitation and vegetation cover across different seasons, seasonal data for the specified factors were employed. Consequently, erosion susceptibility maps were generated on a seasonal basis. Pairwise comparison tables revealed that precipitation, lithology, and slope emerged as the dominant factors contributing to erosion susceptibility in this region. The resultant maps distinctly delineate basins with higher precipitation values, unresistant lithology (such as loess, characterized by high porosity and permeability), and steeper slopes, exhibiting heightened susceptibility to erosion (Archangan and Kalat basins). The credibility of the research findings was examined through on-site observations. The outcomes of this study may provide pertinent insights for decision-makers and planners. This information can be effectively employed in formulating strategies aimed at conserving soil quality in areas vulnerable to erosion hazards.

A novel problem-solving method by multi-computational optimisation of artificial neural network for modelling and prediction of the flow erosion processes

This research aims to forecast, using various criteria, the flow of soil erosion that will occur at a particular geographical location. As for the training dataset, 80% of the dataset from the sample sites, four hybrid algorithms, namely heap-based optimizer (HBO), political optimizer (PO), teaching-learning based optimization (TLBO), and backtracking search algorithm (BSA) combined with artificial neural network (ANN) was used to create an erosion susceptibility model and establishes a unique and original approach. After it was confirmed to be successful, the algorithms were applied to create a susceptibility map for this area, demonstrating the integrity of the results. The AUC values were computed for every optimisation algorithm used in this study. The optimal estimated accuracy indices for populations of 450 were determined to be 0.9846 using the BSA-MLP training databases. The maximum AUC value for the HBO-MLP training databases with different swarm sizes was 0.9736. A swarm size of 350–300 is considered optimal for forecasting erosion susceptibility mapping in hybrid models. With the same swarm size constraints, the AUC value for training in the TLBO-MLP scenario was 0.996. After 150 swarm size conditions were used to train the PO-MLP model, the AUC values were 0.9845. According to these findings, the TLBO-MLP and PO-MLP algorithms worked best with populations of 50 and 150, respectively.

Enhanced morphometric analysis for soil erosion susceptibility mapping in the Godavari river basin, India: leveraging Google Earth Engine and principal component analysis

ABSTRACT Morphometric analysis of a drainage basin along with its stream network is indispensable for analysing a river basin’s prevailing geomorphology, geo-hydrology, and erosional susceptibility. In this study, an enhanced morphometric evaluation method using Google Earth Engine (GEE) and Principal Component Analysis (PCA) was used to assess and prioritize 12 sub-basins of the Godavari River basin (GRB), India, based on their erosional susceptibility. The findings of erosional susceptibility were validated using the Sediment Yield (SY) model. Our findings highlight that the sub-basins of Indravati, Pranhita, and Manjira, i.e. 42.42% (132707.4 km2) of the entire GRB are at high risk of erosional susceptibility, requiring immediate implementation of soil conservation measures. Our findings also highlight that 52.71% of the GRB is in the mature stage of geological evolution, wherein erosional landform-building processes are still ongoing. Overall, GRB has a relatively low drainage density with dendritic drainage patterns, permeable soils, and elongated sub-basins of enhanced erosional susceptibility. Moreover, the results of the SY model exhibited 90% agreement with the results of the enhanced morphometric analysis. This study presents an efficient framework for enhanced morphometric analysis that can assist policymakers in the demarcation of priority zones for erosional susceptibility.

Mapping of soil erosion susceptibility using advanced machine learning models at Nghe An, Vietnam

Abstract Soil Erosion Susceptibility Mapping (SESM) is one of the practical approaches for managing and mitigating soil erosion. This study applied four Machine Learning (ML) models, namely the Multilayer Perceptron (MLP) classifier, AdaBoost, Ridge classifier, and Gradient Boosting classifier to perform SESM in a region of Nghe An province, Vietnam. The development of these models incorporated seven factors influencing soil erosion: slope degree, slope aspect, curvature, elevation, Normalized Difference Vegetation Index (NDVI), rainfall, and soil type. These factors were determined based on 685 identified soil erosion locations. According to SHapley Additive exPlanations (SHAP) analysis, soil type emerged as the most significant factor influencing soil erosion. Among all the developed models, the Gradient Boosting classifier demonstrated the highest prediction power, followed by the MLP classifier, Ridge classifier, and AdaBoost, respectively. Therefore, the Gradient Boosting classifier is recommended for accurate SESM in other regions too, taking into account the local geo-environmental factors.

Gully erosion susceptibility maps and influence factor analysis in the Lhasa River Basin on the Tibetan Plateau, based on machine learning algorithms

The environment of the Tibetan Plateau is under stress due to the warming climate and increased human activity, which also increases the risk of soil erosion. Gully erosion is one of the main environmental problems in the Tibetan Plateau. Taking Lhasa River Basin as a representative, investigating the spatial variation and influencing factors in this area will help to understand the current status and mechanism of gully erosion. However, gully erosion factor analysis has not been carried out at the regional scale. Therefore, three machine learning algorithms, including Random Forest (RF), Support Vector Machine, and Logistic Regression, were used to analyze 2224 samples and 18 influencing factors and detect the gully erosion susceptibility in the Lhasa River Basin. Seventy percent of the samples were used to train the model and rank the influencing factors by importance, and 30% were used to assess the prediction ability of the model. Approximately 15%–20% of the study area has a high or very high gully erosion susceptibility. The slopes of the lower Lhasa River and the Nyenchen Tanglha valleys were identified as areas with a high gully erosion susceptibility. The RF model exhibits the best performance, with area under the curve (AUC) values of 0.969 and 0.894 for the training and testing datasets, respectively. The main factors affecting the occurrence of gully erosion were determined to be the elevation, normalized difference vegetation index, relief, human footprint, mean annual temperature, and distance to the residential area. Elevation has the greatest effect on gully erosion, which can be explained as elevation indirectly controls gully erosion by affecting human activities and rainfall. The findings of this study provide insights into the effects of human activities, climate and permafrost on soil erosion and can serve as a basis for regional land use planning and gully erosion prevention and control in the Tibetan Plateau.

Gully erosion susceptibility mapping and prioritization of gully-dominant sub-watersheds using machine learning algorithms: Evidence from the Silabati River (tropical river, India)

Gully erosion is a severe environmental issue that poses threats to agriculture, human safety, habitats, infrastructure, and soil integrity. Selecting the right machine learning model is vital for accurate Gully Erosion Susceptibility Mapping (GESM) due to varying environmental hazard performance. This study employed a comparative analysis of two machine learning techniques, Random Forest (RF) and Extreme Gradient Boost (XGBoost), to develop a highly precise GESM for the Silabati watershed (India). The analysis incorporated 24 controlling factors and examined a dataset of 460 sample points, with equal representation of gullies and non-gullies. Variance Inflation Factors (VIF) and Information Gain Ratio (IGR) techniques were applied to assess multicollinearity test among the controlling factors. Lithology, elevation, distance from the road, LULC, geomorphology, rainfall, drainage density, and coarse fragments emerged as crucial factors in determining GESM. Statistical tests, including the Kappa index, Root Mean Square Error (RMSE), Accuracy (ACC), Mean Absolute Error (MAE), Coefficient of Determination (R2), and Receiver Operating Characteristic (ROC), were employed to evaluate the RF and XGB models on training and testing data. Both models demonstrated strong performance, with the XGBoost and RF models achieving ROC values of 84.1% and 83.2%, respectively. The applied quantile classification method resulted in the creation of five distinct GESMs, categorized as very high (VH), high (H), moderate (M), low (L), and very low (VL). We found that the very high GESM areas of the watershed were 4.10% in the XGBoost model and 4.61%. in the RF model. Out of the 26 sub-watersheds, the results have identified five sub-watersheds as highly prioritized for sustainable management. Therefore, the present study provides an accurate gully erosion identification using advanced models, offering valuable insights for policymakers to proper management implementation.

Soil erosion susceptibility mapping in Bangladesh

This study aims to draw a scientific framework for plotting soil erosion susceptibility in the Chittagong Hill Tracts of Bangladesh by comparing existing approaches. Data-driven machine learning techniques (including Classification and Regression Tree (CART), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Random Forest (RF)) and a knowledge-based approach (AHP) are used in this study to pinpoint areas of Chittagong that are particularly susceptible to soil erosion while taking into account 18 soil erosion-regulating parameters. Furthermore, the effectiveness of the selected data-driven machine learning models and knowledge-based models was assessed by utilizing soil erosion and non-erosion sites. When evaluating the fidelity of each model using the ROC and AUC, the RF model was shown to be the most accurate and predictive. There is no poor performer among these models; all have AUCs greater than 67 % (RF = 0.86, ANN = 0.73, SVM = 0.67, CART = 0.67, and AHP = 0.82). According to the findings of the Random Forest model, approximately 71.55 percent of the area exhibited a moderate level of susceptibility to soil erosion. In relation to the land area, the high and low zones accounted for 16.91 percent and 11.54 percent, respectively. The specific area shares of 2256.25, 9548.08, and 1539.67 square kilometers were attributed to the high, moderate, and low danger zones, respectively. The best models' results after comparing models of data-driven and knowledge-based approaches can help to estimate soil erosion risk zones and provide insight into establishing appropriate policies to minimize this issue. In addition, the methods used in this research might be applicable to assessing the vulnerability and risk of soil erosion events in other areas. As they begin long-term planning to reduce soil erosion, local authorities and policymakers will find the study's results on practical policies and management options quite helpful.

Binary Logistic Regression applied to erosion susceptibility mapping in the Southern Amazon

Problems with soil erosion by water wind in the Brazilian Amazon are intensifying as the forest is replaced by agricultural production. Deforestation, burning, logging, and advancing the agricultural frontier have altered the soil-vegetation balance. In this context, the analysis of soil erosion susceptibility is one of the most significant challenges to developing long-term sustainability strategies and policies. Based on the above, the present study used the principles of Statistical Modeling - Logistic Regression - to develop and validate a model for analysis of susceptibility to soil erosion using 14 environmental factors. The study was carried out in a hydrographic sub-basin with 330 km2, located in the south of the State of Rondônia in the western Amazon, which combines characteristics of intense anthropic activity, loss of fertile soil, gullies, and silting of rivers. The study area has rainfall above 2000 mm year-1, they are transcurrent shear zones, predominant relief forms are flat to slightly convex tops, drainage networks are dendritic in an exorheic system, vegetation cover is composed of areas of forests and natural or regenerated forest fragments, agriculture is destined to annual crops. Livestock is extensive, with a predominance of small rural properties. The logistic regression model showed satisfactory results with an AUC of 0.888 and global accuracy was 0.77. The variables with the most significant effect on the equation were NDVI, erosivity, and TST. The mapping found that 57.71% of the study area is in places susceptible to soil loss due to water events.

Gully erosion mapping susceptibility in a Mediterranean environment: A hybrid decision-making model

Gully erosion is one of the main natural hazards, especially in arid and semi-arid regions, destroying ecosystem service and human well-being. Thus, gully erosion susceptibility maps (GESM) are urgently needed for identifying priority areas on which appropriate measurements should be considered. Here, we proposed four new hybrid Machine learning models, namely weight of evidence -Multilayer Perceptron (MLP- WoE), weight of evidence –K Nearest neighbours (KNN- WoE), weight of evidence - Logistic regression (LR- WoE), and weight of evidence - Random Forest (RF- WoE), for mapping gully erosion exploring the opportunities of GIS tools and Remote sensing techniques in the El Ouaar watershed located in the Souss plain in Morocco. Inputs of the developed models are composed of the dependent (i.e., gully erosion points) and a set of independent variables. In this study, a total of 314 gully erosion points were randomly split into 70% for the training stage (220 gullies) and 30% for the validation stage (94 gullies) sets were identified in the study area. 12 conditioning variables including elevation, slope, plane curvature, rainfall, distance to road, distance to stream, distance to fault, TWI, lithology, NDVI, and LU/LC were used based on their importance for gully erosion susceptibility mapping. We evaluate the performance of the above models based on the following statistical metrics: Accuracy, precision, and Area under curve (AUC) values of receiver operating characteristics (ROC). The results indicate the RF- WoE model showed good accuracy with (AUC = 0.8), followed by KNN-WoE (AUC = 0.796), then MLP-WoE (AUC = 0.729) and LR-WoE (AUC = 0.655), respectively. Gully erosion susceptibility maps provide information and valuable tool for decision-makers and planners to identify areas where urgent and appropriate interventions should be applied.

GIS based gully erosion susceptibility mapping using Frequency Ratio (FR) model in Dumka district, Jharkhand, India.

GIS based gully erosion susceptibility mapping using Frequency Ratio (FR) model in Dumka district, Jharkhand, India.

Integration of High-Accuracy Geospatial Data and Machine Learning Approaches for Soil Erosion Susceptibility Mapping in the Mediterranean Region: A Case Study of the Macta Basin, Algeria

Erosion can have a negative impact on the agricultural sustainability and grazing lands in the Mediterranean area, especially in northern Algeria. It is useful to map the spatial occurrence of erosion and identify susceptible erodible areas on large scale. The main objective of this research was to compare the performance of four machine learning techniques: Categorical boosting, Adaptive boosting, Convolutional Neural Network, and stacking ensemble models to predict the occurrence of erosion in the Macta basin, northwestern Algeria. Several climatologic, morphologic, hydrological, and geological factors based on multi-sources data were elaborated in GIS environment to determine the erosion factors in the studied area. The conditioning factors encompassing rainfall erosivity, slope, aspect, elevation, LULC, topographic wetness index, distance from river, distance from roads, clay mineral ratio, lithology, and geology were derived via the integration of topographic attributes and remote sensing data including Landsat 8 and Sentinel 2 within a GIS framework. The inventory map of soil erosion was created by integrating data from the global positioning system to locate erosion sites, conducting extensive field surveys, and analyzing satellite images obtained from Google Earth through visual interpretation. The dataset was divided randomly into two sets with 60% for training and calibrating and 40% for testing the models. Statistical metrics including sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (ROC) were used to assess the validity of the proposed models. The results revealed that machine learning and deep learning, as well stacking ensemble techniques, showed outstanding performance with accuracy over 98% with sensitivity 0.98 and specificity 0.98. Policy makers and local authorities can utilize the predicted erosion susceptibility maps to promote sustainable use of water and soil conservation and safeguard agricultural activities against potential damage.